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- W2479061566 abstract "This chapter discusses nonparametric estimation for stationary processes and nonparametric tests for stochastic processes. It reviews such estimates when the observations are dependent. It also discusses some results on estimation of marginal density for stationary Markov processes and stationary mixing processes. The asymptotic results for density estimation in general for dependent observations are essentially the same as those in the case of independent observations, as long as some type of asymptotic independence is present in the process. The chapter also discusses the behavior of robust nonparametric estimators for dependent data and the asymptotic behavior of the Hodges–Lehmann estimator for location parameters when the observations are from a stationary mixing process. It discusses the kernel method of density estimation, the method using orthogonal expansions, and the method of delta sequences. The chapter explains nonparametric test theory for discrete parameter stochastic processes." @default.
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- W2479061566 date "1980-01-01" @default.
- W2479061566 modified "2023-10-17" @default.
- W2479061566 title "Nonparametric Inference for Stochastic Processes" @default.
- W2479061566 doi "https://doi.org/10.1016/b978-0-12-080250-0.50018-x" @default.
- W2479061566 hasPublicationYear "1980" @default.
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